The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models have largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this paper, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, time dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model, as specific examples.

中文翻译：

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使用 R-INLA 的竞争风险联合模型

在联合模型领域取得的方法论进步很多。尽管如此，竞争风险联合模型的情况在很大程度上被忽视了，尤其是从从业者的角度来看。在竞争风险联合模型的相关工作中，高斯线性纵向序列和成比例的特定原因风险函数等假设仍未受到挑战。在本文中，我们提供了一个基于 R-INLA 的框架，以统一的方式应用竞争风险联合模型，使得非高斯纵向数据、空间结构、时间相关样条和各种潜在关联结构，仅举几例，都是在我们的方法中接受。我们的动机源于 SANAD 试验，该试验表现出非线性纵向轨迹和治疗失败的竞争风险。